Evaluating configuration and parametric diversity in simulation-driven powertrain computational synthesis with autonomous control tuning
Year: 2025
Type: article
Abstract: Purpose Computational design synthesis (CDS) provides a systematic means to explore the design space of complex systems. However, the scope of exploration in many CDS studies is biased by limited parametrization, where component parameters remain fixed or arbitrarily assigned. This paper investigates the influence of configuration and parametric diversity on the effectiveness of design-space exploration in vehicle powertrain synthesis. Design/methodology/approach A simulation-driven CDS framework is developed with an autonomous control tuning mechanism integrated to ensure consistent evaluation of the synthesized topologies. Powertrain topologies are synthesized by randomly interconnecting port-compatible components and assigning parameters from predefined ranges. Two topology sets are generated, one emphasizing configuration diversity and another emphasizing parametric diversity, to analyze their impact on exploration. Findings The results indicate that parameter initialization strongly influences perceived performance. A topology’s parameter assignment defines its effective position in design space, and poor initialization can bias evaluation outcomes even with structural diversity. Research limitations/implications The framework is limited to sequential topologies, which makes it easier to explore the entire design space but also makes the results harder to generalize. Practical implications The groundwork established here impacts the development of generative topology synthesis frameworks, enabling autonomous generation, control tuning and simulation of systems. Originality/value The novelty is the autonomous control tuning integrated in a CDS workflow, alongside the investigation into the influence of configuration and parametric diversity on the effectiveness of design-space exploration.
Source: Engineering Computations
Authors: Rohail Malik, Muhammad Ahmad, Jari Vepsäläinen
Institution: Aalto University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Funder: Business Finland
OpenAlex ID: W4417488252